[go: up one dir, main page]

Kim et al., 2009 - Google Patents

An algorithm that improves speech intelligibility in noise for normal-hearing listeners

Kim et al., 2009

View HTML
Document ID
6071871146687695284
Author
Kim G
Lu Y
Hu Y
Loizou P
Publication year
Publication venue
The Journal of the Acoustical Society of America

External Links

Snippet

Traditional noise-suppression algorithms have been shown to improve speech quality, but not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using the ideal binary mask, an algorithm is proposed that decomposes the input signal into time …
Continue reading at pubs.aip.org (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination
    • G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0202—Applications
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/065—Adaptation
    • G10L15/07—Adaptation to the speaker
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00-G10L21/00
    • G10L25/78—Detection of presence or absence of voice signals
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification
    • G10L17/04—Training, enrolment or model building
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification
    • G10L17/26—Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/003—Changing voice quality, e.g. pitch or formants
    • G10L21/007—Changing voice quality, e.g. pitch or formants characterised by the process used
    • G10L21/013—Adapting to target pitch
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signal, using source filter models or psychoacoustic analysis

Similar Documents

Publication Publication Date Title
Kim et al. An algorithm that improves speech intelligibility in noise for normal-hearing listeners
Li et al. Factors influencing intelligibility of ideal binary-masked speech: Implications for noise reduction
Wang Time-frequency masking for speech separation and its potential for hearing aid design
Parikh et al. The influence of noise on vowel and consonant cues
Healy et al. An algorithm to improve speech recognition in noise for hearing-impaired listeners
Stern et al. Hearing is believing: Biologically inspired methods for robust automatic speech recognition
Cooke A glimpsing model of speech perception in noise
Liu et al. Bone-conducted speech enhancement using deep denoising autoencoder
Kim et al. Improving speech intelligibility in noise using environment-optimized algorithms
Chen et al. Contributions of cochlea-scaled entropy and consonant-vowel boundaries to prediction of speech intelligibility in noise
Lai et al. Multi-objective learning based speech enhancement method to increase speech quality and intelligibility for hearing aid device users
Das et al. Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding
Monaghan et al. Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners
Yoo et al. Speech signal modification to increase intelligibility in noisy environments
Li et al. The contribution of obstruent consonants and acoustic landmarks to speech recognition in noise
Roman et al. Pitch-based monaural segregation of reverberant speech
Sadjadi et al. Blind spectral weighting for robust speaker identification under reverberation mismatch
Wang et al. Speech enhancement for cochlear implant recipients
Keshavarzi et al. Use of a deep recurrent neural network to reduce wind noise: Effects on judged speech intelligibility and sound quality
Huber et al. Objective assessment of a speech enhancement scheme with an automatic speech recognition-based system
Li et al. Comparative intelligibility investigation of single-channel noise-reduction algorithms for Chinese, Japanese, and English
Dong et al. Towards real-world objective speech quality and intelligibility assessment using speech-enhancement residuals and convolutional long short-term memory networks
Patil et al. Marathi speech intelligibility enhancement using I-AMS based neuro-fuzzy classifier approach for hearing aid users
Osses et al. A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
Kang et al. Deep learning-based speech enhancement with a loss trading off the speech distortion and the noise residue for cochlear implants